Abstract
Deep learning technology provides novel solutions for localization in complex scenarios. Conventional methods generally suffer from performance loss in the long-distance over-the-horizon (OTH) scenario due to uncertain ionospheric conditions. To overcome the adverse effects of the unknown and complex ionosphere on positioning, we propose a deep learning positioning method based on multistation received signals and bidirectional long short-term memory (BiLSTM) network framework (SL-BiLSTM), which refines position information from signal data. Specifically, we first obtain the form of the network input by constructing the received signal model. Second, the proposed method is developed to predict target positions using an SL-BiLSTM network, consisting of three BiLSTM layers, a maxout layer, a fully connected layer, and a regression layer. Then, we discuss two regularization techniques of dropout and randomization which are mainly adopted to prevent network overfitting. Simulations of OTH localization are conducted to examine the performance. The parameters of the network have been trained properly according to the scenario. Finally, the experimental results show that the proposed method can significantly improve the accuracy of OTH positioning at low SNR. When the number of training locations increases to 200, the positioning result of SL-BiLSTM is closest to CRLB at high SNR.
Highlights
High-precision location of the over-the-horizon (OTH) target is a critical issue in the fields of space target surveillance and navigation
A deep learning method for OTH positioning called SL-bidirectional long short-term memory (BiLSTM) was proposed. e proposed SL-BiLSTM encodes location features in signal data based on BiLSTM structure and obtains a position estimation model by training on reference locations. e number of reference positions is limited in practical applications
Simulation experiments verify that SL-BiLSTM has higher positioning accuracy in OTH scenarios, while conventional localization methods generally suffer performance losses
Summary
High-precision location of the over-the-horizon (OTH) target is a critical issue in the fields of space target surveillance and navigation. Wireless localization theories and methods have made great progress in recent years, there are still many challenging problems existing in OTH scenarios [1]. E location precision is strictly limited by the accuracy and matching degree of measuring parameters. Another type of algorithm is direct position determination (DPD) [7,8,9,10,11], which overcomes the shortcomings in two-step position methods and significantly improves positioning performance under low signal-noise ratio conditions. DPD can integrate channel information (such as ionospheric structure) into the position estimation model [12]. Uncertain ionospheric conditions and lower signal-noise ratio make it difficult for the existing position algorithms to model complex channel scenes. Uncertain ionospheric conditions and lower signal-noise ratio make it difficult for the existing position algorithms to model complex channel scenes. us, OTH positioning still faces huge challenges even with current advanced DPD algorithms
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
